Large Language Models (LLMs)

# Using OpenAI's GPT API
import openai
from typing import List, Dict

class GPTClient:
    def __init__(self, api_key: str, model: str = "gpt-4"):
        self.client = openai.OpenAI(api_key=api_key)
        self.model = model
    
    def generate_text(self, prompt: str, max_tokens: int = 1000, 
                     temperature: float = 0.7) -> str:
        """Generate text using GPT model"""
        response = self.client.chat.completions.create(
            model=self.model,
            messages=[{"role": "user", "content": prompt}],
            max_tokens=max_tokens,
            temperature=temperature
        )
        return response.choices[0].message.content
    
    def chat_completion(self, messages: List[Dict[str, str]], 
                       temperature: float = 0.7) -> str:
        """Multi-turn conversation with GPT"""
        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            temperature=temperature
        )
        return response.choices[0].message.content
    
    def function_calling(self, messages: List[Dict], functions: List[Dict]):
        """Use GPT with function calling capabilities"""
        response = self.client.chat.completions.create(
            model=self.model,
            messages=messages,
            functions=functions,
            function_call="auto"
        )
        return response.choices[0]

# Example usage
gpt = GPTClient(api_key="your-api-key")

# Simple text generation
result = gpt.generate_text("Explain quantum computing in simple terms")

# Multi-turn conversation
messages = [
    {"role": "system", "content": "You are a helpful AI assistant"},
    {"role": "user", "content": "What is machine learning?"},
    {"role": "assistant", "content": "Machine learning is..."},
    {"role": "user", "content": "Can you give me an example?"}
]
response = gpt.chat_completion(messages)